A new method combines machine learning with mixed-effects models for better repeated measurement analysis.
problem Inference of linear coefficients in partially linear mixed-effects models with complex interactions and high-dimensional variables.
method Double machine learning approach to estimate nonparametrically nonlinear variables, then use standard linear mixed-effects techniques to estimate the linear coefficient.
result The estimated fixed effects coefficient converges at the parametric rate and is semiparametrically efficient.
GBMixed boosts mixed models for clustered data, estimating mean and variance flexibly.
problem Flexible estimation of mean and variance components in clustered data.
method Gradient Boosting framework for linear mixed models with likelihood-based gradients.
result GBMixed accurately recovers complex nonlinear fixed effects and covariances.
We develop an HMC algorithm to easily marginalize random effects in LMMs.
problem Bayesian inference in LMMs is challenging, especially marginalizing random effects.
method Developed an HMC algorithm to marginalize random effects in LMMs efficiently.
result Marginalization is always beneficial when applicable and improves various models, especially cognitive science models.
Many scientific and engineering challenges -- ranging from pharmacokinetic drug dosage allocation and personalized medicine to marketing mix (4Ps) recommendations -- require an understanding of the unobserved heterogeneity in order to develop the best decision making-processes. In this paper, we develop a hypothesis te…
Neural models improve GLMMs for complex data.
problem Nonlinear relationships in grouped data.
method Replaced linear function with neural networks.
result Improved performance on synthetic and real-world data.
A scalable model for high-dimensional longitudinal data.
problem Modeling high-dimensional, non-linear, time-varying longitudinal data.
method LMM-VAE, combining linear mixed models and amortized variational inference.
result Competitive performance across simulated and real-world datasets.
New Riemannian optimization improves variance estimation in mixed models.
problem Challenges in estimating variance parameters in linear mixed models due to constraints.
method Formulated as an optimization problem on a Riemannian manifold, using Riemannian gradient and Hessian.
result Yields higher quality variance parameter estimates compared to existing methods.
Gradient Boosted Mixed Models estimate mean and variance components for clustered data.
problem Limited flexibility in linear mixed models for complex settings.
method Gradient Boosting extended to mixed models with likelihood-based gradients and flexible base learners.
result Accurate recovery of variance components and improved predictive accuracy.
This paper concerns the development of an inferential framework for high-dimensional linear mixed effect models. These are suitable models, for instance, when we have n repeated measurements for M subjects. We consider a scenario where the number of fixed effects p is large (and may be larger than M), but the n…
Proposes an efficient algorithm for mHealth that makes real-time physical activity suggestions.
problem Training complex models efficiently in real-time mHealth applications.
method Streamlined empirical Bayes procedure for fitting linear mixed effects models.
result Improves accuracy and speed over state-of-the-art approaches in mHealth applications.
Paper extends LME models to allow sign constraints on coefficients with SDTN random effects.
problem Inference with sign constraints on random effects in LME models.
method Proposes SDTN distribution for random effects and develops likelihood-based approaches for estimation.
result Proposed constrained model improves real-world interpretations and achieves satisfactory performance.
GLMM trees identify subgroups with different growth patterns in longitudinal data.
problem Identifying subgroups with distinct growth trajectories in longitudinal studies.
method Extended GLMM trees for longitudinal data.
result Extended GLMM trees outperform other methods in accuracy and speed.
The likelihood for the parameters of a generalized linear mixed model involves an integral which may be of very high dimension. Because of this intractability, many approximations to the likelihood have been proposed, but all can fail when the model is sparse, in that there is only a small amount of information availab…
Method improves regression models using unlabeled data.
problem Improving predictive performance of regression models with limited labeled data.
method Mixed semi-supervised generalized-linear-regression with different mixing mechanisms.
result Integrating unlabeled data consistently improves predictive performance.
BayesBoost combines boosting and Bayesian methods for linear mixed models, improving uncertainty estimation and variable selection.
problem Lack of straightforward uncertainty estimation for parameters in high-dimensional linear mixed models.
method BayesBoost: Combines boosting and Bayesian inference for linear mixed models.
result Improves uncertainty estimation and variable selection in linear mixed models.
New method optimizes mixed integer optimization for hierarchical modeling of clustered and longitudinal data.
problem Optimizing subset selection in hierarchical models with clustered and longitudinal data.
method Distribution-free mixed-integer optimization approach for cluster-aware regression.
result The method efficiently solves problems within minutes and outperforms traditional models in generating sparse solutions with high predictive power.
We solve structure learning for cyclic linear causal models using observational data.
problem Learning the structure of cyclic linear causal models from observational data.
method Assuming simple graphs, we use a criterion for distributional equivalence and implement a greedy search method.
result We show that simple cyclic models are of expected dimension and justify score-based methods for structure learning.
Study improves maize yield prediction using BNs with mixed-effects models.
problem Limited causal inference in agronomic data models.
method Integrates random effects into Bayesian networks, leveraging hierarchical data structure.
result Significantly reduces maize yield prediction error from 28% to 17%.
Paper develops federated GLMM algorithms for analyzing hierarchical data.
problem Analyzing hierarchical data with non-independent observations in a federated setting.
method Developed two federated GLMM algorithms using Laplace and Gaussian Hermite approximations.
result Federated GLMM can handle hierarchical data and achieve comparable or superior performance.
MOMENT selects and estimates mixed-effects models using moment identities.
problem Selecting and estimating random-effects covariance matrix and fixed-effects coefficients in multiresponse linear mixed-effects models.
method MOMENT is a stage-wise moment-based framework that reduces the random-effects selection problem to a smooth constrained convex optimization problem.
result MOMENT performs competitively and can outperform separate univariate analyses for correlated responses.
Combines boosting with Gaussian process and mixed effects models.
problem Model misspecifications and independence assumptions in boosting.
method Relaxes zero or linearity assumption in Gaussian process and mixed effects models, and independence assumption in boosting.
result Increased prediction accuracy compared to existing approaches.
Gradient EM converges exponentially to optimal solution in agnostic mixtures.
problem Fitting k parametric functions to given data points without a generative model. method Gradient EM algorithm for agnostic mixtures of arbitrary parametric functions.
result Gradient EM converges exponentially to population loss minimizers with high probability.
New method selects sparse predictors in large LMMs.
problem Sparse learning for LMMs is computationally infeasible for large datasets.
method Developed an ℓ0 regularized method with coordinate descent and local search algorithms. result Method selects thousands of predictors in seconds to minutes.
Mix-IRLS solves imbalanced mixed linear regression problems efficiently.
problem Imbalanced mixed linear regression problems.
method Sequential robust regression approach.
result Mix-IRLS outperforms other methods on imbalanced mixtures and real-world datasets.
Causal inference concerns the identification of cause-effect relationships between variables. However, often only linear combinations of variables constitute meaningful causal variables. For example, recovering the signal of a cortical source from electroencephalography requires a well-tuned combination of signals reco…
Paper addresses online identification and clustering for mixed linear regression models.
problem Online identification and clustering of mixed linear regression models.
method Introduces two online identification algorithms based on the EM principle, proving global convergence without i.i.d. data assumptions.
result Global convergence of the proposed algorithms for mixed linear regression models.
Integrates neural encoders into GLMMs for multimodal data analysis.
problem Scalable Bayesian inference for GLMMs assumes low-dimensional tabular predictors and does not handle high-dimensional modalities.
method Jointly learns modality-specific neural encoders with GLMM objective, performs variance-corrected stochastic-gradient MCMC.
result Preserves interpretable fixed and random effects while scaling to large longitudinal datasets.
NoLimits.jl: Flexible and Composable Nonlinear Mixed-Effects Modeling in Julia
problem Flexible and composable nonlinear mixed-effects modeling
method Macro-based modeling language and unified interface
result Substantially expand the range of nonlinear mixed-effects models
Paper analyzes agnostic learning of mixed linear regression without generative models.
problem Learning mixed linear regression without assuming stochastic generation.
method Expectation Maximization (EM) and Alternating Minimization (AM) algorithms.
result AM and EM algorithms converge to population loss minimizers under standard conditions.
New method for Bayesian inference on large datasets.
problem Scalable sampling for Bayesian generalized linear mixed models on large datasets.
method Mirror Langevin dynamics with data subsampling, post-processing for variance estimation.
result Asymptotic, order-wise correct estimation of posterior variance.
The study prevents model collapse in overparameterized linear regression by mixing real and synthetic labels.
problem Preventing model collapse in overparameterized linear regression.
method Iterative mixing of real and synthetic labels, deriving generalization error formulae.
result Optimal mixing ratio converges to the reciprocal of the golden ratio for isotropic features.
Study compares neural and statistical models for Parkinson's disease progression from voice data.
problem Difficult statistical analysis of longitudinal voice biomarkers due to subject correlation, small cohorts, and varied disease trajectories.
method Evaluated Neural Mixed Effects (NME), Generalized Neural Network Mixed Models (GNMMs), and semi-parametric Generalized Additive Mixed Models (GAMMs).
result GAMMs achieve stronger predictive performance and retain interpretable smooth effects and subject-level structure.
Develops RF-GLS for binary geospatial data.
problem Challenges in extending RF to binary geospatial data.
method Proposes RF-GLS for binary data, embedding it in generalized mixed effects models.
result Establishes consistency of RF-GP for mean function and covariate effect estimation.
metabeta uses neural networks to speed up Bayesian mixed-effects regression.
problem Bayesian mixed-effects regression is computationally expensive.
method metabeta is a neural network model that pre-trains to estimate posterior distributions.
result metabeta achieves comparable performance to MCMC at a fraction of the time.
We derive streamlined mean field variational Bayes algorithms for fitting linear mixed models with crossed random effects. In the most general situation, where the dimensions of the crossed groups are arbitrarily large, streamlining is hindered by lack of sparseness in the underlying least squares system. Because of th…
The optimization of expensive to evaluate, black-box, mixed-variable functions, i.e. functions that have continuous and discrete inputs, is a difficult and yet pervasive problem in science and engineering. In Bayesian optimization (BO), special cases of this problem that consider fully continuous or fully discrete doma…
Novel GLMMNet model tackles high-cardinality categorical features in actuarial applications.
problem Inadequate encoding methods for high-cardinality categorical features in actuarial data.
method Generalised Linear Mixed Model Neural Network (GLMMNet) integrating a generalised linear mixed model in a deep learning framework.
result GLMMNet often outperforms or performs comparably with entity embedded neural networks, providing transparency.
New Krylov subspace methods speed up mixed-effects models with crossed random effects.
problem Slow computations for high-dimensional crossed random effects in mixed-effects models.
method Krylov subspace-based methods for generalized mixed-effects models with cross effects.
result Speedups by factors of up to 10,000 in computations for mixed-effects models.
Spectral methods improve signal recovery in mixed GLMs with precise asymptotics.
problem Estimating multiple signals from unlabeled observations in mixed GLMs.
method Developed exact asymptotics for spectral methods in a proportional regime.
result Optimized spectral method combined with a linear estimator minimizes estimation error.
Study integrates reliability constraints into generation planning models.
problem Challenges in integrating reliability constraints with generation planning models.
method Leverages a weighted oblique decision tree (WODT) technique to embed reliability verification constraints.
result Demonstrates effectiveness in achieving reliable and optimal planning solutions.
A scalable Bayesian inference method for mixed-effects models in systems biology.
problem Scalable Bayesian inference for complex hierarchical mixed-effects models in systems biology.
method Constructing amortized approximations of likelihood and posterior distributions, refined for each individual dataset.
result Our method is both fast and competitive in statistical accuracy compared to exact pseudomarginal Bayesian inference.
SBMs learn manifold-like structures by mixing samples with a non-conservative field.
problem How SBMs learn data distributions on low-dimensional manifolds.
method Investigating linear approximations and subspaces of local feature vectors during diffusion.
result SBMs mix samples by a non-conservative field within the manifold, maintaining manifold-like structure.
Improved DSSMs for easier interpretable latent variables.
problem Complex and hard-to-interpret latent variables in DSSMs.
method Simplified predictive decoder and shrinkage priors.
result Interpretable latent variables improve forecasting performance.
We consider the problem of solving mixed random linear equations with k components. This is the noiseless setting of mixed linear regression. The goal is to estimate multiple linear models from mixed samples in the case where the labels (which sample corresponds to which model) are not observed. We give a tractable a…
Improved spatial prediction for massive datasets using SME model.
problem Efficiently estimating parameters in massive spatial datasets.
method Spatial Mixed Effects (SME) model with AECM algorithm for flexibility.
result Improved estimation without sacrificing prediction accuracy.
Mixed effects (ME) models inform a vast array of problems in the physical and social sciences, and are pervasive in meta-analysis. We consider ME models where the random effects component is linear. We then develop an efficient approach for a broad problem class that allows nonlinear measurements, priors, and constrain…
Paper proposes methods to improve SVM classifiers in noisy data scenarios.
problem Improving SVM classifiers when training data contains label noise.
method Mixed Integer Linear and Non Linear models with relabeling and clustering.
result Effective methods improve SVM performance in noisy data scenarios.
Proposes a new model for context-dependent decision-making.
problem Constant preference parameters in decision models are too rigid.
method Introduces Context-aware Bayesian mixed multinomial logit model using neural networks.
result Models context-dependent intra-respondent heterogeneity effectively.